With the advent of statistical approaches used for application processing, benefits determinations, zoning, credit, voting and hiring, data objects (such as resumes, social profiles, assessments, voice, and video interview modeling) have been used to predict success. These approaches are prone to negative influence when an adverse impact exists specific to a protected class in the training set. The training set is the selection of data that is used for the computer to learn which features are most important for predicting performance. For example, if minority sales professionals are given less than ideal sales regions compared to their majority counterparts, an adverse impact may exist in the performance training set. This impact, if untreated, could be mimicked in the final model that was trained on this performance data.